Understanding the functional impact of copy number alterations in breast cancer 

using a network modeling approach

Sriganesh Srihari1*,#Murugan Kalimutho5# , Samir Lal2 , Jitin Singla3 , Dhaval Patel3, Peter T. Simpson2,4 , Kum Kum Khanna5 and Mark A. Ragan1

1Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia

2 The University of Queensland, UQ Centre for Clinical Research, Brisbane, QLD 4029, Australia

3 Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India

4 The University of Queensland, School of Medicine, Brisbane, QLD 4006, Australia

QIMR-Berghofer Institute of Medical ResearchBrisbane, QLD 4006, Australia

*Contact: sriganesh [dot] m [dot] s [at] gmail [dot] com

# joint first authors

NEW BOOK!! Computational prediction of protein complexes from protein interaction networks, published by ACM Books and Morgan & Claypool.

Please cite: Srihari S, Kalimutho M et al., Understanding the functional impact of copy number alterations in breast cancer using a network modelling approach. Molecular Biosystems 2016; 10.1039/C5MB00655D. 

If you are looking to follow up on BRF2 -- e.g. by designing a small-molecular inhibitor, or by testing BRF2 in other cell line and in vivo models -- we would love to hear from you, and be willing to help you further with any bioinformatic analysis that may be required. (Contact: sriganesh [d] m [d] s [at] gmail [d] com).

Supplementary figures and data

1/ Survival plots for k = 8, 9, 10 clusters using cis- , trans- and cis- and trans-associated genes

Figure 2

Figure 2 of manuscript: Kaplan-Meier plots of disease-specific survival (truncated at 15 years) for clusters identified using cis- (917), trans- (663) and by combining cis- and trans-associated (1527) genes (arranged horizontally) for k = 8, 9, 10 clusters (arranged vertically) from the Discovery dataset (998 tumours). Log-rank test p-value in each of the cases was significant (p < 0.0001). The cluster numbers and colours are not comparable across plots.

Figure 2i: Survival proportions using cis- and trans-associated genes for k = 10

2/ Cytoscape network file (.cys) of trans-associated genes along with their Gene Ontology enrichment information

3/ Experimental validation of RFC4 as a potential biomarker. 

The effect is more prominent in ER-negative tumours compared to ER-positive tumours, indicating that RFC4 could be a marker for ER-negative/aggressive tumours. However, since normal MFC10A cells are also affected, targeting RFC4 could induce some toxicity.

4/ Experimental validation of BRF2 as a therapeutic target for ER-/HER2+ breast tumours.

BRF2 shows higher protein expression for ER-negative/HER2-enriched tumours compared to normal MCF10A cells, and the knockdown of BRF2 induces significantly more cell death in MDA-MB-453 and -361 cells compared to normal cells. Therefore, BRF2 could be a very good therapeutic target to pursue.

5/ Source codes (in C++ and R)

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Australian National Health and Medical Research Council (NHMRC) grant 1028742 to PTS and MAR.